Machine Learning

How Intelligent Automation is Powering Machine Vision

Incorporating artificial intelligence into the day-to-day operations of firms has become increasingly common. Having intelligent automation running operations is advantageous in many ways. It brings efficiency and effectiveness to a business's operations. Applications of intelligent automation today include machine vision, a key technology in the business world.  Machine vision plays a crucial role in transforming images into digital data. Computers analyze the data to arrive at vital conclusions needed to make business and non-business decisions. A combination of artificial intelligence features and machine vision will make computer vision more effective.

This article discusses machine vision and intelligent automation's relevance to machine vision. It also describes the computer vision technology used and what to expect of computer vision technology and intelligent automation in the future.

What is machine vision?

Machine vision refers to the ability of a computer to see and comprehend images. But in this case, it uses a camera to capture the images of objects within its vicinity. Data from the taken picture is subject to analysis to determine the next action. A machine vision system constitutes a set of components including sensors, frame-grabber, cameras, software, algorithms, and output devices, such as screens.

The system initiates the process when the sensor detects a specific object within its surrounding. It triggers a light source to illuminate the product, and the camera takes a picture of the same. The frame-grabber is a digitizing device that converts the image into digital data. The software receives the data file and analyzes it based on specific criteria. It scans for quality, defects, authenticity, and similarity to other products. A screen shows the analyzed data, which is now helpful in the decision-making process for a firm.

How is intelligent automation relevant to machine vision?

Machine vision stands out because it implements human-like capabilities by seeing and perceiving its surroundings. But it becomes even more powerful by incorporating artificial intelligence. AI enables machines to think, understand, and learn when observing their environment. Combining AI with machine vision technology empowers company systems with a decision-making capacity.

Intelligent automation makes machine vision systems teachable. A camera attached to a computer vision system learns to take images selectively by evaluating what is relevant and what is not. It can differentiate specific features on products and classify them into specific groups.

Intelligent automation enables machine vision to learn from previous experiences and avoid repeating previous mistakes. Sightech Eyebot is an example of a machine vision system that combines intelligent automation with machine vision. It learns the form and features of a product presented to it. A shape Sightech Eyebot classifies these products by shape and identifies those that deviate from the norm. Incorporating AI makes machine vision more efficient because it reduces redundancy and enhances its data processing speeds.

Where is computer vision technology used today?

Medicine

Companies are increasingly adopting computer vision, and medicine is interestingly one of the fields venturing into this technology. Surgical operations are now using computer vision to improve medical imaging for preoperative planning. The images ensure that surgeons identify small details that could influence their diagnostic procedures and prognosis of the condition. Here, computer vision systems use 3-dimension high-resolution endoscopes to identify the pathology and make surgical incisions with precision.

Sign language translation

Sign language translation systems have also adopted computer vision. The machine vision system uses a camera to capture the gestures and body movements of individuals with a disability. It then uses its software to analyze the data into understandable information. This technology is valuable amongst the hearing impaired and people with language disabilities. Computer vision has made it possible for individuals with physical impairments to live more comfortable lives. They can now socialize with other people with ease because of the improved communication process.

Product scanning

The business world is responsible for most of the machine vision system applications available today. Computer vision enables businesses to supervise their products more effectively. It identifies any defects in products before their distribution. The company corrects these issues and avoids the extra costs associated with product returns from the final consumers. Besides, content supervision of products reduces the cumbersome workload that manual laborers have to bear. Manual supervision is time-consuming and costly for businesses.

Product monitoring and evaluation

Using computer vision enhances the speed of product evaluation and monitoring. A firm can supervise a large number of products and information within a short time. For instance, firms checking for product authenticity use visual algorithms in their computer vision systems to countercheck products against those deemed to be legitimate. Also, they can use these capabilities to identify illegal goods, such as explosive products and drugs.

Computer vision makes the sorting process efficient for both on-premise and online merchandise. Its application ensures that individuals and organizations do not suffer unnecessary financial losses.

Inventory control and management

Business organizations also use computer vision in inventory control and management. This system can read barcodes on products, reducing the process of entering data into a computer. In supermarkets, for instance, computer vision makes the shopping process quick. Individuals pass their goods on the barcode reader, which captures the data quickly. The computer screen displays the product cost, and one pays. Computer vision eliminates the delays at the cashier counter and improves the sales turnover in a retail business.

Product traceability

In the pharmaceutical industry and other companies that deal in sensitive products, computer vision aids product traceability. Highly-regulated products require monitoring to avoid misuse by people. Computer vision helps pharmaceutical firms track the ingredients of a given product and monitor its expiration date.

E-commerce

In e-commerce, machine vision makes it possible to search for products online using pictures. Computer vision benefits consumers by improving their shopping experience. It eliminates potential errors that individuals make when buying replica products. Besides, it removes the ambiguity that people experience when reading product descriptions. Once you upload a specific image, the visual computing system in computer vision identifies it.

Agriculture

Farmers use the computer vision system to harvest their farm products quickly. A computer vision algorithm classifies the farm products based on their quality, size, and shapes. The algorithm then directs robots to pluck the farm produce. Computer vision has been used to harvest crops in some regions of the world and proved efficient.

In India, for example, computer vision has been used to harvest grapes. AI-powered computer vision robots grade the quality of the fruits before picking them. Computer vision also facilitates supervision of the crop in the field using drones. Drones can detect unfavorable weather conditions for their growth, ensuring that the farmers take appropriate measures to avoid loss of yields. Besides, drones fitted with computer vision also help detect ailing crops for spraying purposes.

The future of computer vision technology and intelligent automation

With time, computer vision and intelligent automation technologies will become increasingly inseparable. Companies like Tesla and Waymo are venturing into autonomous cars, giving a glimpse of what the future for these technologies could become.

Self-driving systems use computer vision coupled with artificial intelligence to assume the function of a driver. They detect movements in 360 degrees to watch out for other vehicles, pedestrians, and cyclists. Also, they follow the traffic flow and regulations. However, there are some challenges with autonomous cars, as noted from a driverless Tesla car that caused crashes. The advancements made so far indicate that self-driving is possible with further improvements in computer vision and intelligent automation.

Computer vision and technology combined with intelligent automation will guarantee an automated future. Companies will no longer rely on human labor to drive a competitive advantage anymore. Instead, employees will be overseeing the running of automated systems. The manufacturing processes will become more efficient because of improved data collection and analysis. It will become easier to track products during their assembly and speed up their packing and distribution.

Besides, the medical-surgical field will experience a revolution towards the adoption of virtual operations. Currently, only a few centers are undertaking robot-assisted surgeries, but that could change in a few decades. Improved computer vision and intelligent automation systems will enhance the efficiency of the virtually conducted surgeries. Surgeons working remotely can operate a patient using a robot, which will reduce the challenge of medical specialists' shortage.

Conclusion

Intelligent automation is the key to unlocking the full potential of machine vision. Machine vision detects object features in the surroundings. However, without intelligent automation, it is prone to repeat mistakes. Intelligent automation makes machine vision systems teachable. They can learn from their previous errors and keep improving.

With technological advancements, intelligent automation and machine vision have become centers for important innovations such as autonomous cars and virtual surgeries. They have also become vital in making manufacturing and distribution processes in organizations more efficient. Machine vision enables companies to avoid losses associated with illegal and counterfeit products. Computer vision technology and intelligent automation promise a better future in automation.